3 Questions About AI That Nontechnical Employees Should Be Able to Answer

Executive Summary

There are three important questions that any member of your team should be able to answer about AI: How does artificial intelligence work? What is it good at? And what should it never do? Team members who aren’t responsible for building an AI system should nonetheless know how it processes information and answers questions. Learning what machine learning is good at quickly helps someone to see what machine learning is not good at. Problems that are novel, or which lack meaningful data to explain them, remain squarely in the realm of human specialties. Help your employees to understand this difference by showing them tools they already use that are powered by AI, either within the organization or outside it (such as social media advertising or streaming service recommendations). These examples will help team members to understand AI’s enormous potential, but also its limitations. Just because machine learning can solve a problem does not mean it should do so.

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Articles about artificial intelligence often begin with an intention to shock readers, referencing classic works of science fiction or alarming statistics about impending job losses. But I think we get closer to the heart of AI in 2018 when we think about small and mundane ways in which AI makes work just a little easier. And it’s not necessarily the AI experts in your organization who will identify these mundane problems that AI can help solve. Instead, employees throughout the organization will be able to spot the low-hanging fruit where AI could make your organization more efficient. But, only if they know what AI is capable of doing, and what it should never do.

For example, I manage the finances for a team that travels very often, and I’ve been grateful for the intelligent guesswork that my expenses software extracts from receipts using machine learning: the merchant’s name, the dollar amount spent, taxes, and likely expense categorization. Finding opportunities for this kind of clever improvement, saving human time and energy, is not just a leadership challenge. It’s a search best undertaken by as many people within the organization as possible.

A fast-growing area of artificial intelligence is machine learning, in which a computer program improves its answers to a question by creating and iterating algorithms based on data. It is often regarded as the kind of technology that only the most clever and most mathematically-minded people can understand and work with. Indeed, those who work day-to-day building machine learning programs will tend to have postgraduate degrees in computer science. But machine learning is a technological tool like any other: it can be understood on various levels, and can still be used by those whose understanding is incomplete. People do not need to know how to fly a plane to be able to spot sensible new airline routes. Instead, they need to know approximately what a plane can and cannot do. For instance, lay people might also have ideas about what planes should not be used for, which could result in positive outcomes such as reducing aircraft noise in the middle of cities or limiting costly flights for very short journeys.

When leaders in companies, non-profits, or governments invest in artificial intelligence, much of their attention goes to hiring machine learning experts, or paying for tools. But this misses a critical opportunity. For organizations to get the most that they can from AI, they should also be investing in helping all of their team members to understand the technology better. Understanding machine learning can make an employee more likely to spot potential applications in her own work. Many of the most promising uses for machine learning will be humdrum, and this is where technology can be at its most useful: saving people time, so that they can concentrate on the many tasks at which they outperform machines. An executive assistant who has a better understanding of machine learning might suggest that calendar software learn more explicitly from patterns that develop over time, reminding him when his boss has not met with a team member for an unusually long time. A calendar that learns patterns could give an executive assistant more time for the human specialties of his job, such as helping his boss to manage a team.

So, what should all of your employees be learning about AI? There are three important questions that any member of your team should be able to answer: How does artificial intelligence work? What is it good at? And what should it never do? Let’s look at each in more detail:

How does it work? Team members who aren’t responsible for building an AI system should nonetheless know how it processes information and answers questions. It’s particularly important for people to understand the differences between how they learn and how a machine “learns.” For example, a human trying to analyze one million data points will need to simplify it in some way in order to make sense of it — perhaps by finding an average, or creating a chart. A machine learning algorithm, on the other hand, can use every individual data point when it makes its calculations. They are “trained” to spot patterns using an existing set of data inputs and outputs. Because data is fundamental to a machine’s ability to provide useful answers, a manager should ensure that her team members should have some basic data literacy. This means helping people to understand what numbers are telling us, and the biases and errors that might be hidden within them. Understanding data — the fuel of AI — helps people to understand what AI is good at.

What is it good at? Machine learning tools excel when they can be trained to solve a problem using vast quantities of reliable data, and to give answers within clear parameters that people have defined for them. My expenses software is a perfect example: it has the receipts of its millions of users to learn from, and it uses them to help predict whether a cup of coffee from Starbucks should be categorized as travel, stationery, or entertainment. Learning what machine learning is good at quickly helps someone to see what machine learning is not good at. Problems that are novel, or which lack meaningful data to explain them, remain squarely in the realm of human specialties. Help your employees to understand this difference by showing them tools they already use that are powered by AI, either within the organization or outside it (such as social media advertising or streaming service recommendations). These examples will help team members to understand AI’s enormous potential, but also its limitations.

What should it never do? Just because machine learning can solve a problem does not mean it should do so. A machine cannot understand, for example, the biases that data reveals, nor the consequences of the advice it gives. There may be some problems that your organization should never ask an AI application to solve. For example, I would not want an algorithm to make the final decision in my company on whom to hire, what to discuss at a board meeting, or how to manage a poorly-performing staff member. If employees have thought about proper ethical limitations of AI, they can be important guards against its misuse.

The organizations that will do best in the age of artificial intelligence will be good at finding opportunities for AI to help employees do their day-to-day jobs better, and will be able to implement those ideas quickly. They will be clear about where to deploy machine learning, and where to avoid it. Alongside their investments in technology, they will remind their teams of the importance of human specialties: supporting colleagues, communicating well, and experimenting with novel ideas. To be ready for pervasive AI, an organization’s whole team will need to be ready too.

Emma Martinho-Truswell is the co-founder and Chief Operating Officer of Oxford Insights, which advises organizations on the strategic, cultural, and leadership opportunities from digital transformation and artificial intelligence.